Sandra Camille Sandoval, Christabel Acquaye, Kwesi Adu Cobbina, Mohammad Nayeem Teli, Hal Daumé Iii. Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers). 2025.

Recent work has highlighted the culturallycontingent nature of commonsense knowledge (Shen et al., 2024).We introduce AMAMMERE (/A:.mA:.mu:.reI/),from the Akan word meaning 'culture.'This test set of 525 multiple-choice questions is designed to evaluate the commonsense knowledge of English LLMs, relative to the cultural contexts of Ghana and the United States.To create AMAMMERE, we select a set of multiplechoice questions (MCQs) from existing commonsense datasets and rewrite them in a multistage process involving surveys of Ghanaian and U.S. participants.In three rounds of surveys, participants from both pools are solicited to ( 1) write correct and incorrect answer choices, ( 2) rate individual answer choices on a 5-point Likert scale, and (3) select the best answer choice from the newly-constructed MCQ items, in a final validation step.By engaging participants at multiple stages, our procedure ensures that participant perspectives are incorporated both in the creation and validation of test items, resulting in high levels of agreement within each pool.We evaluate several off-the-shelf English LLMs on AMAMMERE. 1 Uniformly, models prefer answers choices that align with the preferences of U.S. annotators over Ghanaian annotators.Additionally, when test items specify a cultural context (Ghana or the U.S.), models exhibit some ability to adapt, but performance is consistently better in U.S. contexts than Ghanaian.As large resources are devoted to the advancement of English LLMs, our findings underscore the need for culturally adaptable models and evaluations to meet the needs of diverse English-speaking populations around the world.